This article introduces a new method for detection of atrial fibrillation (AFib) using a support vector machine (SVM). AFib could lead to heart failure and stroke and thus an AFib early detection is very important. In this article, an SVM and variabilities of electrocardiographic heart rate are employed to detect AFib. Radial basis functions (RBF) is utilized for SVM. Different SVM constructions are tested to find the best one. Furthermore, two features of electrocardiogram are examined as the inputs of SVM. Using clinical electrocardiogram, the proposed method find the performance of 95.81 %, 98.44% and 97.50% in terms of sensitivity, specificity and accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Atrial fibrillation detection using support vector machine


    Beteiligte:
    Nuryani, Nuryani (Autor:in) / Harjito, Bambang (Autor:in) / Yahya, Iwan (Autor:in) / Lestari, Anik (Autor:in)


    Erscheinungsdatum :

    01.11.2015


    Format / Umfang :

    230984 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Automatic Paroxysmal Atrial Fibrillation Detection Method Based on Grey Information Measurement

    Zhang, Rui / Wang, Jibin | British Library Online Contents | 2018



    Atrial Fibrillation During an Exploration Class Mission

    Lipsett, Mark / Hamilton, Douglas / Lemery, Jay et al. | NTRS | 2011


    Atrial Fibrillation Among the NASA Astronaut Corps

    Charvat, Jacqueline / Lee, Stuart / Stenger, Michael et al. | NTRS | 2020


    Traffic Incident Detection Using Multiple-Kernel Support Vector Machine

    Xiao, Jianli / Liu, Yuncai | Transportation Research Record | 2012